Introduction: Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology.
Objective: This research aimed to identify the geographical traceability for different parts of W. cocos.
Methods: The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm-1 , 6300-5150 cm-1 and 4450-4050 cm-1 of original spectra and the second-order derivative (SD) spectra preprocessed.
Results: From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified.
Conclusion: Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation.
Keywords: FT-NIR; PLS-DA; ResNet; Wolfiporia cocos; geographical traceability.
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